TyG Index and Cognitive Decline in Non-diabetic Elderly: Evidence from CLHLS 2014-2018

TyG Index and Cognitive Decline in Non-diabetic Elderly: Evidence from CLHLS 2014-2018 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article TyG Index and Cognitive Decline in Non-diabetic Elderly: Evidence from CLHLS 2014-2018 Diwen Shen, Qi Qian, Yang Liu, Hailong Yang, Junjun Liu, Xiangdong Du This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7178589/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective The relationship between insulin resistance (IR) and susceptibility to cognitive decline remains unclear, with conflicting research findings. This nationwide retrospective analysis sought to examine the relationship between cognitive impairment and the triglyceride-glucose (TyG) index, a surrogate marker for IR, among older Chinese individuals without diabetes. Methods Data analysis was conducted using information derived from the 2014–2018 cohort of the Chinese Longitudinal Healthy Longevity Survey (CLHLS), comprising 988 non-diabetic adults whose mean age was 79.78 years (SD = 9.21). The study population included participants, 572 were male (57.89%) and 416 were female (42.11%). The Mini-Mental State Examination (MMSE) was employed to evaluate cognitive function. Multivariate Cox proportional hazards models were utilized to assess the association between the TyG index and the likelihood of cognitive impairment. To investigate potential threshold effects, a two-piecewise Cox regression approach was implemented. Furthermore, the study incorporated interaction and stratified analyses, taking into account factors such as age, gender, marital status, exercise habits, smoking, and alcohol consumption. Results After a 4-year follow-up period, 201 participants (20.3%) developed cognitive impairment, despite having normal cognition at baseline. Controlling for relevant variables, the multivariate Cox regression analysis did not reveal a statistically meaningful link between cognitive decline and the TyG score (HR = 0.77, 95% CI: 0.56–1.06, P = 0.106). However, smoothing plots suggested that the relationship between cognitive impairment and the TyG index was non-linear, with a turning point identified at 7.57. Above this inflection point, a negative association was observed (HR = 0.67, 95% CI: 0.50–0.91, P = 0.009), whereas no notable association was found below it (P = 0.75). Conclusions Our research uncovers a nuanced, non-linear association linking the TyG index to cognitive decline among elderly individuals without diabetes. These results offer valuable insights with implications for informing public health strategies and policy development. insulin resistance retrospective cohort study triglyceride glucose index cognitive impairment older Figures Figure 1 Figure 2 Figure 3 1. Background Over the past few decades, life expectancy has significantly increased, thanks to remarkable advancements in modern medicine and improved living conditions, especially in emerging nations. Consequently, the elderly population is projected to experience prolonged longevity in the future. However, this rise in life expectancy has led to an increase in age-related disorders [ 1 ]. In China, this demographic shift poses a pressing public health challenge. According to data from China's most recent national census, by 2021, the population aged 60 and above had grown to 264 million, constituting 18.70% of China's total inhabitants. This represents a 5.44% increase compared to the sixth national census, with further growth expected in the coming decades [ 2 ]. By 2040, projections suggest that elderly individuals will comprise approximately 22.6% of the total population [ 3 ]. The global phenomenon of population aging has brought cognitive impairment into the spotlight as a significant public health concern. With the continuous increase in life expectancy, the aging process accelerates, putting a considerable number of older adults at a higher risk of developing cognitive impairment. This condition, characterized by declines in memory, attention, language, and other cognitive functions, represents an intermediary state between normal aging and dementia [ 4 ]. In the context of a rapidly aging global population, cognitive impairment has emerged as a significant determinant of compromised health [ 5 ]. Its association with reduced life expectancy underscores the substantial burden it places on public health infrastructure. Particularly in China, the prevalence rates of dementia and cognitive decline among individuals aged 60 and above were 6.04% and 15.54%, respectively, in 2020 [ 6 ]. Among those over 65 years old, mild cognitive impairment (MCI) affects a range from 3–19% of the population. Alarmingly, more than half of these individuals progress to dementia within a five-year period following diagnosis [ 7 ]. For older adults, MCI serves as a risk factor for the progression to severe cognitive impairment or dementia, increasing the burden on individuals and caregivers and making independent living challenging. Currently, no definitive treatment for dementia exists, although two anti-amyloid therapies recently received FDA approval, which may slow disease progression [ 8 ]. Consequently, pinpointing the elements that predispose individuals to cognitive decline is essential in efforts to avert dementia in the aging population. Insulin resistance (IR) has emerged as a key factor in metabolic syndrome, with recent studies demonstrating indicated a strong association between the progression of Alzheimer's disease (AD) and metabolic abnormalities [ 9 , 10 ]. According to the World Health Organization (WHO), disrupted glucose metabolism represents a major global health issue. Projections indicate that obesity and type 2 diabetes mellitus (T2DM) will affect 500 million individuals worldwide by 2023, reflecting a substantial increase in these conditions over the past five decades [ 11 ]. IR, characterized by impaired cellular response to insulin, is closely linked to T2DM. Beyond inducing hyperglycemia, IR is implicated in the development of neurodegenerative disorders later in life, including AD [ 12 ]. Epidemiological studies reveal significant similarities in the risk factors and pathophysiological mechanisms underlying T2DM and AD. IR and hyperglycemia initiate cascades of processes that contribute to neuronal death and cognitive decline [ 13 ]. The majority of research indicates that IR adversely affects cognitive function, with prior studies linking insulin resistance, measured using the Homeostasis Model Assessment (HOMA-IR) method, to increased risk of AD [ 14 ], impaired global cognition [ 15 ], and accelerated cognitive decline [ 16 ]. Increasing epidemiological data demonstrate the usefulness of IR in predicting cognitive impairment. Furthermore, clinical trials have shown that intranasal insulin treatment is beneficial for patients with AD and early cognitive decline [ 17 ]. However, conflicting results have emerged from some research examining the link between IR and cognitive impairment. Euser et al. [ 18 ], through meticulous examination of data from two distinct prospective studies involving a collective cohort of 8,447 participants, concluded that heightened insulin resistance and fasting glucose levels do not correlate with diminished cognitive function in older individuals without a history of diabetes. It is noteworthy that although a growing number of investigations explore the potential implications of reduced central nervous system (CNS) insulin signaling in the pathogenesis of neurodegenerative disorders, diminished insulin signaling has also demonstrated efficacy in enhancing lifespan regulation and delaying age-related processes. Remarkably, the attenuation of insulin signaling has the capacity to extend lifespan and delay the onset of protein-aggregation-induced harm, despite its potential to precipitate diabetes [ 19 ]. Moreover, an expanding body of literature has elucidated that restraining insulin activity augments life expectancy across diverse organisms, including primates and vertebrates. Hence, the paradoxical nature of insulin's effects appears to be substantiated [ 20 ]. Extended longitudinal and large-scale cohort studies are necessary to address the inconsistent evidence regarding how IR affects cognitive function over time. The China Longitudinal Healthy Longevity Survey (CLHLS) is a prospective, dynamic cohort consisting of older Chinese individuals residing in communities. Since its establishment in 1998, the CLHLS has been regularly monitored every 2–3 years. Therefore, utilizing CLHLS data to explore IR's influence on cognitive decline among non-diabetic elderly population, our research seeks to contribute population-based insights into the health implications of IR. Few longitudinal investigations have been conducted to date on the connection between IR and] cognitive impairment in older Chinese adults. Hence, using the longitudinal dataset from the CLHLS, covering the years 2014 to 2018, our investigation aims to assess the relationship between cognitive decline and an alternative measure of IR: the triglyceride glucose (TyG) index. 2. Methodology 2.1. Participants and study framework Our research utilized data from the CLHLS, an ongoing community-based longitudinal investigation that has been going on since 1998. Employing a targeted, disproportionate sampling strategy, the CLHLS aimed to encompass nearly half of China's cities and counties across 23 provinces, thereby ensuring an adequate representation of older adults [ 21 ]. Between 1998 and 2018, eight follow-up surveys were conducted. To mitigate cohort attrition due to mortality and loss of follow-up, new participants were periodically recruited during each survey wave. In order to identify health risk factors among individuals aged 65 and above, particularly those aged 80 and older, the CLHLS's main goal is to gather extensive data on lifestyle, health status, and demography. Follow-ups take place every three to four years. For this study, trained physicians from public health officials and community hospitals from the Centers for Disease Control and Prevention (CDC) conducted in-home, in-person interviews. Previous research has demonstrated that the CLHLS maintains an exceptionally low non-response rate, with an average data attrition rate of 4.85% per wave, comparable to major Western cohorts, and generally upholds acceptable data quality standards. Data from the CLHLS's 2014–2018 cohort were used in the study. Eligibility criteria required participants to have complete data on glucose and triglyceride levels and to have reached or exceeded 65 years of age by 2014. Exclusion criteria encompassed individuals with diabetes or cognitive impairment at baseline, those lacking Mini-Mental State Examination (MMSE) scores in 2014 or 2018, and individuals who could not be contacted for follow-up or had decreased by 2018. Ultimately, the study cohort consisted of 988 individuals. The enrollment and follow-up procedures are detailed in Fig. 1 . The CLHLS was granted ethical approval by the institutional review boards at both Duke University and Peking University (with approval number IRB00001052–13074), in accordance with the Declaration of Helsinki. All participants provided informed consent when time of their enrollment. Interviewers, trained by the Chinese CDC, conducted in-home interviews using a standardized questionnaire to ensure the accurate collection of detailed participant data and to provide attentive care for the elderly. The questionnaire is accessible on the internet at the Peking University Open Research Data platform: https://opendata.pku.edu.cn/ . 2.2. Data collection and variable measures Under rigorous quality control protocols, face-to-face questionnaire surveys were employed to collect sociodemographic characteristics, health behaviors, and comorbid conditions during the 2014 baseline survey. Sociodemographic factors included age, sex, educational attainment (0, 1–6, and ≥ 7 years of schooling), and marital status (married, single, divorced, or widowed). The health behaviors assessed encompassed current exercise routines, smoking status, and alcohol consumption. The comorbid conditions recorded included hypertension, diabetes, cancer, heart disease, stroke, and other cardiovascular diseases. Cognitive performance during each survey was evaluated utilizing a validated cognitive assessment instrument, the Mini-Mental State Examination (MMSE). This tool was previously validated through pilot survey interviews. To better accommodate the elderly population in China, several items in the Chinese version of the MMSE were cross-culturally adapted, with their validity and reliability rigorously confirmed [ 21 ]. The MMSE comprises six domains: orientation, registration, attention, language, memory, and visuoconstructional skills, with total scores ranging from 0 to 30. Cognitive impairment was categorized based on educational attainment: scores below 17 indicated illiteracy, scores below 20 corresponded to one to six years of education, and scores below 24 were indicative of seven or more years of education. The study exclusively enrolled participants with normal baseline cognitive function to assess cognitive impairment. The endpoint for follow-up was defined as the onset of cognitive impairment in subjects. The time span was determined by calculating the follow-up period, marking the period over which cognitive changes were monitored, which began with the 2014 baseline survey and ended on the most current follow-up date. Blood pressure measurements were obtained using a standard mercury sphygmomanometer after at least 15 minutes of seated rest. The right arm's blood pressure was measured twice, and the average of these readings was used to determine systolic and diastolic blood pressures (SBP and DBP, respectively) by averaging two readings. The body mass index (BMI) was calculated based on conventional anthropometric measurements provided by each patient, with the formula BMI = weight/(height)^2, where height is in meters (m) and weight in kilograms (kg). Following an overnight fast, venous blood samples were drawn and used to measure blood biomarkers such as lipid profile components (triglycerides, total cholesterol, high-density lipoprotein cholesterol) and fasting blood glucose. Using the logarithmic transformation of TG and FBG, the TyG index [ 22 ] was derived using a specific formula: TyG = Ln [FBG (mg/dL) × TG (mg/dL)/2]. 2.3. Statistical analysis To assess the normality of all continuous variables, the researchers employed the one-sample Kolmogorov-Smirnov test. To analyze the differences among the various groups, a one-way ANOVA was applied for continuous variables, while categorical variables were assessed using a chi-squared test. To investigate the association between the TyG index and cognitive decline, we employed Cox regression models. For sensitivity analysis, the TyG index was categorized into tertiles. To evaluate potential multicollinearity among independent variables, we calculated the variance inflation factor (VIF). Any variables with a VIF exceeding 5 were deemed to exhibit multicollinearity and were subsequently removed from the final Cox regression analysis. For the final multivariable model, potential confounders were included if they influenced the TyG index estimates related to cognitive impairment by more than 10% or had a significance level below 0.10 in univariable analysis [ 23 ]. In cases where the TyG index and cognitive impairment showed a non-linear association, we utilized a smoothing plot and a two-piecewise linear regression model, implemented through the Generalized Estimating Equation (GEE) approach. The EmpowerStats ( http://www.empowerstats.com , X&Y Solutions, Inc., Boston, Massachusetts, USA) and R software ( http://www.r-project.org , The R Foundation) were employed to conduct all statistical analyses. A two-tailed test was used to assess statistical significance, with the threshold set at a p-value less than 0.05. 3. Results From the CLHLS cohort that ran from 2014 to 2018, a subset of 988 participants was selected. These individuals, among the initial 7192 participants, had complete TyG index data and a MMSE score of 24 or above in 2014 (Fig. 1 ). Participants were excluded if their TyG index data were incomplete (n = 4963), they were lost to follow-up (n = 327), died in 2018 (n = 813), were younger than 65 years (n = 69), lacked MMSE scores in 2014 (n = 115), had cognitive impairment in 2014 (n = 79), or lacked total MMSE scores in 2018 (n = 108). 3.1. Participants' demographic and clinical characteristics Table 1 displays the initial characteristics of the study participants, categorized by TyG index tertiles. The study cohort included 988 individuals, whose mean age was 79.78 ± 9.21 years, with males comprising 572 (57.89%) of the sample. The TyG index tertiles were found to be significantly correlated with a number of variables (all P < 0.05), including age, gender, BMI, SBP, DBP, current drinking and smoking status, prevalence of heart disease and hypertension, FBG, TG, TC, HDL-c, and MMSE total scores in 2014. In contrast, no significant associations were found among TyG index tertiles and factors such as years of education, marital status, current exercise habits, history of stroke or cardiovascular disease, cancer, and follow-up duration (all P > 0.05). Table 1 Baseline characteristics of participants Variables Total TyG index tertile P-value T1 (6.24–8.15) T2 (8.16–8.65) T3 (8.66–11.23) N 988 325 325 338 Age (years) 79.78 ± 9.21 80.87 ± 9.47 80.24 ± 9.10 78.30 ± 8.90 < 0.001 Gender < 0.001 Male 572 (57.89%) 222 (68.31%) 189 (58.15%) 161 (47.63%) Female 416 (42.11%) 103 (31.69%) 136 (41.85%) 177 (52.37%) Years of education 0.556 0 459 (47.47%) 137 (43.63%) 155 (48.74%) 167 (49.85%) 1–6 374 (38.68%) 131 (41.72%) 118 (37.11%) 125 (37.31%) >=7 134 (13.86%) 46 (14.65%) 45 (14.15%) 43 (12.84%) Marital status 0.953 Married 558 (57.88%) 181 (57.46%) 182 (57.59%) 195 (58.56%) Single, divorced, or widowed 406 (42.12%) 134 (42.54%) 134 (42.41%) 138 (41.44%) Current exercising 0.326 No 772 (79.67%) 247 (78.16%) 250 (78.37%) 275 (82.34%) Yes 197 (20.33%) 69 (21.84%) 69 (21.63%) 59 (17.66%) Current smoking 0.023 No 790 (80.53%) 245 (76.09%) 260 (80.75%) 285 (84.57%) Yes 191 (19.47%) 77 (23.91%) 62 (19.25%) 52 (15.43%) Current drinking 0.042 No 797 (81.49%) 249 (77.09%) 266 (83.12%) 282 (84.18%) Yes 181 (18.51%) 74 (22.91%) 54 (16.88%) 53 (15.82%) Hypertension 0.024 No 667 (69.12%) 233 (74.44%) 219 (68.65%) 215 (64.56%) Yes 298 (30.88%) 80 (25.56%) 100 (31.35%) 118 (35.44%) Heart disease 0.002 No 884 (91.13%) 299 (94.92%) 294 (91.59%) 291 (87.13%) Yes 86 (8.87%) 16 (5.08%) 27 (8.41%) 43 (12.87%) Stroke or cvd 0.141 No 907 (93.31%) 302 (95.57%) 296 (91.93%) 309 (92.51%) Yes 65 (6.69%) 14 (4.43%) 26 (8.07%) 25 (7.49%) Cancer 0.557 No 930 (99.25%) 305 (99.03%) 313 (99.68%) 312 (99.05%) Yes 7 (0.75%) 3 (0.97%) 1 (0.32%) 3 (0.95%) BMI (kg/m 2 ) 22.42 ± 3.94 21.43 ± 3.23 22.21 ± 4.26 23.56 ± 3.94 < 0.001 SBP (mmHg) 143.41 ± 22.32 139.86 ± 23.04 144.91 ± 22.60 145.38 ± 20.96 0.002 DBP (mmHg) 81.06 ± 12.43 79.70 ± 13.37 82.15 ± 13.00 81.34 ± 10.75 0.038 FBG (mmol/l) 5.36 ± 1.64 4.53 ± 0.85 5.17 ± 1.03 6.36 ± 2.12 < 0.001 TC (mmol/l) 1.32 ± 0.81 0.73 ± 0.18 1.12 ± 0.24 2.07 ± 0.94 < 0.001 TG (mmol/l) 4.86 ± 0.95 4.55 ± 0.89 4.88 ± 0.92 5.14 ± 0.96 < 0.001 HDL-c (mmol/l) 1.41 ± 0.38 1.56 ± 0.37 1.45 ± 0.34 1.22 ± 0.33 < 0.001 TyG index 8.47 ± 0.62 7.83 ± 0.28 8.40 ± 0.15 9.15 ± 0.42 < 0.001 MMSE total score in 2014 28.22 ± 1.79 28.05 ± 1.86 28.16 ± 1.85 28.44 ± 1.65 0.015 Follow up time (months) 41.64 ± 2.71 41.81 ± 2.82 41.48 ± 2.53 41.64 ± 2.78 0.302 Note: The variables are presented as n (%) or the mean ± SD, TyG: triglyceride glucose; BMI: body mass index; FBG: fasting blood glucose; TC: total cholesterol; TG: triglyceride; HDL-c: high-density lipoprotein cholesterol; SBP: systolic blood pressure; DBP: diastolic blood pressure; MMSE: Mini-Mental State Examination. 3.2. Univariate analysis of cognitive impairment Throughout the observational period spanning 2014 to 2018, cognitive impairment manifested in 201 individuals (20.3%) within the cohort, as recorded over a four-year follow-up duration. The outcomes of our univariate statistical assessment are presented in Table 2 . Age ( HR = 1.08, 95% CI : 1.06–1.09), gender (female) ( HR = 1.84, 95% CI : 1.39–2.44 versus male), and marital status (single, divorced, or widowed) ( HR = 2.65, 95% CI : 1.96–3.57 versus married) were risk factors that showed positive correlations with cognitive impairment; all parameters were statistically significant ( P < 0.05). Conversely, protective elements linked to decreased incidence of cognitive impairment encompassed 1–6 years of education ( HR = 0.30, 95% CI : 0.21–0.43 versus 0 years), exceeding 7 years of education ( HR = 0.15, 95% CI : 0.07–0.32 versus 0 years), habitual physical activity ( HR = 0.59, 95% CI: 0.39–0.88), current tobacco use ( HR = 0.43, 95% CI : 0.27–0.69), continuing alcohol consumption ( HR = 0.45, 95% CI : 0.30–0.75), body mass index ( HR = 0.93, 95% CI : 0.89–0.98), total cholesterol ( HR = 0.78, 95% CI : 0.63–0.96), TyG index ( HR = 0.79, 95% CI : 0.63–0.99), tertiles categorized as T3 for TyG index ( HR = 0.63, 95% CI : 0.45–0.89 versus T1), and MMSE total score in 2014 ( HR = 0.74, 95% CI : 0.69–0.79) (all P < 0.05). Nevertheless, variables including hypertension, heart disease, history of stroke or cardiovascular disease, cancer, SBP, DBP, FBG, triglycerides, HDL-c, and tertiles classified as T2 for TyG index (versus T1) did not exhibit significant associations with cognitive impairment (all P > 0.05). Table 2 Univariate analysis for cognitive impairment Covariate Statistics HR (95% CI) P-value N 988 Age (years) 79.78 ± 9.21 1.08 (1.06, 1.09) < 0.001 Gender Male 572 (57.89%) 1.0 (reference) Female 416 (42.11%) 1.84 (1.39, 2.44) < 0.001 Years of education 0 459 (47.47%) 1.0 (reference) 1–6 374 (38.68%) 0.30 (0.21, 0.43) =7 134 (13.86%) 0.15 (0.07, 0.32) < 0.001 Marital status Married 558 (57.88%) 1.0 (reference) Single, divorced, or widowed 406 (42.12%) 2.65 (1.96, 3.57) < 0.001 Current exercising No 772 (79.67%) 1.0 (reference) Yes 197 (20.33%) 0.59 (0.39, 0.88) 0.009 Current smoking No 790 (80.53%) 1.0 (reference) Yes 191 (19.47%) 0.43 (0.27, 0.69) < 0.001 Current drinking No 797 (81.49%) 1.0 (reference) Yes 181 (18.51%) 0.45 (0.30, 0.75) 0.001 Hypertension No 667 (69.12%) 1.0 (reference) Yes 298 (30.88%) 1.11 (0.82, 1.50) 0.490 Heart disease No 884 (91.13%) 1.0 (reference) Yes 86 (8.87%) 1.05 (0.63, 1.75) 0.853 Stroke or CVD No 907 (93.31%) 1.0 (reference) Yes 65 (6.69%) 1.37 (0.78, 2.41) 0.274 Cancer No 930 (99.25%) 1.0 (reference) Yes 7 (0.75%) 0.00 (0.00, Inf) 0.993 BMI (kg/m 2 ) 22.42 ± 3.94 0.93 (0.89, 0.98) 0.004 SBP (mmHg) 143.41 ± 22.32 0.99 (0.99, 1.00) 0.113 DBP (mmHg) 81.06 ± 12.43 1.00 (0.98, 1.01) 0.451 FBG (mmol/l) 5.36 ± 1.64 0.98 (0.89, 1.07) 0.589 TC (mmol/l) 1.32 ± 0.81 0.78 (0.63, 0.96) 0.022 TG (mmol/l) 4.86 ± 0.95 0.88 (0.76, 1.02) 0.080 HDL-c (mmol/l) 1.41 ± 0.38 0.91 (0.63, 1.32) 0.627 TyG index 8.47 ± 0.62 0.79 (0.63, 0.99) 0.041 TyG index tertiles T1 325 (32.89%) 1.0 (reference) T2 325 (32.89%) 0.80 (0.57, 1.11) 0.176 T3 338 (34.21%) 0.63 (0.45, 0.89) 0.008 MMSE total score in 2014 28.22 ± 1.79 0.74 (0.69, 0.79) < 0.001 Note: The variables are presented as n (%) or the mean ± SD, HR: Hazard Ratio; CI: confidence interval; TyG: triglyceride glucose; BMI: body mass index; FBG: fasting blood glucose; TC: total cholesterol; TG: triglyceride; HDL-c, high-density lipoprotein cholesterol; SBP: systolic blood pressure; DBP: diastolic blood pressure; MMSE: Mini-Mental State Examination. 3.3. Relationships between cognitive impairment and TyG index Initially, TyG index was assessed as a continuous measure. An inverse correlation emerged between the TyG index and cognitive impairment ( P 0.05). For further analysis, we stratified into tertiles for further examination. Notably, participants in the highest tertile demonstrated a reduced likelihood of cognitive decline compared to those in the lowest tertile after adjustment for multivariate covariates, yielding a hazard ratio of 0.60 (95% CI : 0.38–0.94, P = 0.027). The findings suggest a potential curvilinear association between TyG index levels and cognitive impairment, as elaborated in Table 3 . Table 3 Relationship between TyG index and cognitive impairment in different models Variable Unadjusted Model Model Ⅰ Model Ⅱ N HR (95%CI) P-value HR (95%CI) P-value HR (95%CI) P-value TyG index 988 0.79 (0.63, 0.99) 0.041 0.80 (0.59, 3.74) 0.160 0.77 (0.56, 1.06) 0.106 TyG index tertile T1 (6.24–8.15) 325 1.0 (reference) 1.0 (reference) 1.0 (reference) T2 (8.16–8.65) 325 0.80 (0.57, 1.11) 0.176 0.75 (0.49, 1.14) 0.179 0.78 (0.51, 1.20) 0.254 T3 (8.66–11.23) 338 0.63 (0.45, 0.89) 0.008 0.65 (0.42, 1.00) 0.052 0.60 (0.38, 0.94) 0.027 P for trend < 0.001 < 0.001 0.02 Abbreviations: HR, hazard ratio; CI, confidence interval; Unadjusted Model adjusted for none; Model Ⅰ adjusted for age, sex, marital status, and years of education; Model Ⅱ adjusted for age, sex, marital status, years of education, BMI, current exercising, current smoking, current drinking. 3.4. Analyses of the non-linear relationship Figure 2 , derived from generalized additive models, illustrates a curvilinear relationship linking TyG index levels to cognitive impairment (P for non-linearity = 0.011). An inflection point was found with a TyG index value of 7.57 using a segmented Cox regression analysis with two components. After this critical threshold, every one-unit elevation in the TyG index correlates with a significant 33% decrease in cognitive impairment likelihood (HR = 0.67, 95% CI: 0.50 to 0.91, P = 0.009). For values beneath the inflection point, the analysis revealed no statistically meaningful association (HR = 64.39, 95% CI: 0.44 to 9367.71, P = 0.101). Table 4 illustrates this difference. The inflection point confidence intervals (7.54 to 7.86) were estimated using bootstrapping techniques. Among the participants, 935 had a TyG index of 7.57 or higher, whereas only 53 had an index below this threshold. The small sample size for cases with a TyG index below 7.57 (n = 53) limits the statistical robustness of these findings, necessitating cautious interpretation of the results regarding cognitive impairment in this subgroup. Table 4 The results of two-piecewise Cox regression model Inflection points of TyG index HR 95%CI P -value Inflection point 7.57 7.54 to 7.86 = 7.57 slope 2 (n = 935) 0.67 0.50 to 0.91 0.009 slope 2 – slope 1 0.01 0.00 to 1.65 0.077 Log likelihood ratio test 0.011 Effect: cognitive impairment in 2018; Cause: TyG index; adjusted for age, sex, marital status, years of education, BMI, current exercising, current smoking, current drinking. Slope 1 and slope 2 are the slope coefficients for the segment before and after the inflection point. 3.5. Subgroup analyses of cognitive impairment and TyG index Figure 3 showcases a subgroup analysis indicating a consistent pattern across diverse demographic and lifestyle variables, including gender (male, female), age ( 0.05, suggesting no significant interaction effects. 4. Discussion This study marks the inaugural cohort investigation in China examining the association between the TyG index and cognitive decline in elderly non-diabetic individuals, providing several novel insights. After adjusting for covariates, the Cox regression model demonstrated a 40% reduction in cognitive impairment likelihood for participants in the upper tertile (T3) relative to those in the lowest (T1) when stratifying the TyG index into tertiles. Furthermore, our findings revealed a curvilinear association linking TyG index values to cognitive impairment risk, with a notable turning point at 7.57. For TyG values below this threshold, the relationship was statistically insignificant, while to the right, it became distinctly negative. These findings highlight the complex nature of the dynamics between IR and the risk of cognitive impairment within this population, providing valuable implications for public health strategies and policy development. Insulin resistance is characterized by impaired glucose oxidation and uptake in response to insulin, decreased glycogen synthesis, and a reduced ability to inhibit lipid oxidation in peripheral tissues, including fat deposits, hepatic cells, and muscle fibers [ 24 ]. To compensate for the decreased effectiveness of insulin, the body secretes excessive amounts of insulin, resulting in hyperinsulinemia [ 25 ]. This disruption in energy homeostasis correlates with alterations in genetic activity and protein production, which have been linked to elevated levels of triglycerides, the formation of reactive oxygen species (ROS), the secretion of inflammatory mediators, and increased lipolysis [ 26 ]. Intravenous glucose tolerance tests and euglycemic insulin clamps are considered the gold standard for diagnosing insulin resistance; however, their high cost and invasive nature limit their use in clinical practice [ 27 ]. Consequently, the TyG index, which measures serum triglycerides and fasting blood glucose, has been proposed as a cost-effective and user-friendly indicator of insulin resistance (IR) [ 22 ]. Existing studies have demonstrated a connection linking IR to cognitive deterioration. Systemic IR and its related metabolic disturbances may elevate the likelihood of oxidative damage, cerebrovascular disorders, amyloid β accumulation in the brain, and increased concentrations of inflammatory mediators, such as interleukin-6 and tumor necrosis factor-alpha, which can lead to neurodegeneration [ 28 ]. Furthermore, hyperinsulinemia may impair insulin levels in the cerebrospinal fluid and disrupt brain insulin signaling pathways, leading to reduced mitochondrial activity and abnormal neurosynaptic function [ 29 ]. These alterations ultimately have the potential to compromise cognitive performance. However, the findings from this cohort study suggest that, among a non-diabetic Chinese population, higher baseline levels of insulin resistance are correlated with reduced incidence of cognitive decline over a 4-year period. This finding raises the possibility of an "insulin resistance paradox" in the relationship between insulin resistance in older individuals and cognitive impairment. Specifically, the correlation becomes statistically significant for TyG index values above 7.57. A single-unit rise in the TyG index corresponds to a 33% reduction in the probability of cognitive impairment over the four-year timeframe. The exact pathological mechanisms underlying this inverse relationship remain to be fully understood. Aging poses a significant threat to brain health, primarily through the degradation of white matter, which is manifested as abnormal hyperintensity, compromised white matter integrity around the hippocampus, and cortical atrophy, particularly in the frontal lobes [ 30 ]. The brain regions associated with memory and cognition show substantial age-related reductions, with the most significant changes observed in total brain volume, frontal lobe volume, and medial temporal lobe volume. In the later stages of disease, additional neocortical cell loss occurs [ 31 ]. This leads to clinical and anatomical abnormalities, including overall brain shrinkage, increased cortical thinning in the frontal lobe, ventricular enlargement, increased white matter ischemia, and reduced hippocampal volume [ 32 ]. In elderly individuals, cerebral blood flow gradually decreases, which adversely affects the brain's supply of oxygen and nutrients, thus exacerbating cognitive decline [ 33 ]. Glucose metabolism is the sole energy source for brain tissue, with increased neuroactivity further increasing energy demands [ 34 ]. Insulin resistance in skeletal muscle facilitates the redirection of glucose from peripheral tissues to the brain, which is more energy-dependent and vulnerable compared to other tissues [ 35 ]. Under adverse conditions, these mechanisms may help conserve glucose for cerebral use. Lower insulin resistance has been associated with lower fasting blood glucose levels, waist circumference (WC), body weight, BMI, TC, TG, and LDL-c [ 36 ]. Prolonged hypoglycemia can deprive neurons of essential oxygen and nutrients, potentially leading to neuronal damage. Extended periods of low blood sugar may cause pathological changes in the nervous system, including myelin degradation, synapse loss, and neuronal atrophy [ 37 ]. Considering the known link between lipid metabolism and cognitive decline, it is noteworthy that the brain contains approximately 20% of the body's cholesterol, with myelin-producing oligodendrocytes responsible for about 70% of this amount. These myelinating oligodendrocytes are crucial for multiple neuronal functions, such as enhancing the velocity of information processing, resilience to oxidative stress, and the maintenance of blood-brain barrier integrity [ 38 ]. Given the complex relationship between cognitive impairment and the TyG index, further comprehensive investigations are needed to elucidate the underlying pathological mechanisms. This study has several strengths that address limitations observed in previous research. Specifically, we utilized a large, nationally representative Chinese elderly cohort with an extended follow-up period, providing more robust longitudinal evidence on the relationship between cognitive function and the TyG index among elderly individuals without diabetes. Nevertheless, it's crucial to recognize certain constraints in our research design. Firstly, the restricted availability of biological sample data for the oldest individuals in the CLHLS database resulted in a small sample size for the elderly population studied. Secondly, the study population consisted only of elderly Chinese participants, which may restrict the applicability of the results to the broader Chinese population and other nations or ethnic groups. Thirdly, this study did not evaluate alternative measures of insulin resistance such as homeostasis model assessment of insulin resistance (HOMA-IR) or the revised quantitative insulin sensitivity check index (QUICKI), which are commonly used surrogate measures derived from fasting insulin and glucose data. Fourthly, cognitive function was assessed using the MMSE, which is a validated tool for population-based studies but not a professional diagnostic tool for cognitive impairment. Fifth, we did not examine whether changes in the TyG index throughout the study period correlated with heightened likelihood of cognitive impairment, as our analysis was limited to baseline levels. Sixth, the study population consisted of non-diabetic elderly individuals from China, whose average age was approximately 80 years and an average TyG index of 8.47 ± 0.62. Therefore, additional studies are required to assess whether these results apply to different demographic groups. Lastly, potential unidentified or unquantified variables might affect the observed association between cognitive impairment and the TyG index. However, we adjusted for common confounders in various models, and the robustness of the results remained. In conclusion, this nationwide retrospective cohort study revealed a curvilinear relationship linking the TyG index with cognitive impairment in non-diabetic elderly individuals in China, with a negative correlation observed above a TyG index threshold of 7.57. These findings provide some support for the "insulin resistance paradox" theory. Monitoring and controlling the TyG index may potentially enhance cognitive performance in older individuals without diabetes. Nevertheless, further rigorous multicenter studies are required to confirm these results and investigate the underlying mechanisms, which could aid in risk assessment and primary prevention of cognitive decline in the elderly. Declarations Availability of data and materials The original data used in this paper were derived from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). The data are available from the website: https://opendata.pku.edu.cn/dataverse/CHADS. Processed data described in the manuscript, code book, and analytic code will be made available upon request pending an approval from the corresponding author. Authors' contributions Study Design: Xiangdong Du, Junjun Liu. Investigation: Diwen Shen, Qi Qian, Yang Liu, Hailong Yang. Analysis and interpretation of data: Junjun Liu, Diwen Shen, Qi Qian. Drafting of the manuscript: Junjun Liu, Hailong Yang. Critical revision of the manuscript: Xiangdong Du. Approval of the final version for publication: Junjun Liu, Xiangdong Du, Diwen Shen. Acknowledgments The authors would like to express their gratitude to the participants and staff involved in the data collection and management in the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Competing Interests All the authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This work was supported by the Medical Science and Technology Development Foundation, Nanjing Department of Health (Nos. YKK21216, YKK20184, YKK22264). The funding sources of this study had no role in study design, data collection and analysis, decision to publish, or preparation of the article. Consent for publication Written informed consent for publication was obtained from all participants. Ethics approval and consent to participate The Peking University biomedical ethics commission (IRB0000105224713074) authorized all procedures involving research study participants in the Chinese Longitudinal Healthy Longevity Survey (CLHLS), which is openly accessible, in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants. Clinical trial number Not applicable. References Jin K, Simpkins JW, Ji X, et al. The Critical Need to Promote Research of Aging and Aging-related Diseases to Improve Health and Longevity of the Elderly Population. Aging Dis. 2014;6(1):1–5. 10.14336/AD.2014.1210 . Wang J, Yang Z, Li Y, et al. Status and influencing factors of elder neglect by geriatric nursing assistants in Chinese nursing homes: a cross-sectional survey. Front Med (Lausanne). 2023;10:1273289. 10.3389/fmed.2023.1273289 . Zhu X, Luo Z, Tian G, et al. Hypotension and cognitive impairment among the elderly: Evidence from the CLHLS. PLoS ONE. 2023;18(9):e0291775. 10.1371/journal.pone.0291775 . Rajan KB, Weuve J, Barnes LL, et al. Population estimate of people with clinical Alzheimer's disease and mild cognitive impairment in the United States (2020–2060). Alzheimers Dement. 2021;17(12):1966–75. 10.1002/alz.12362 . Duan J, Lv YB, Gao X, et al. Association of cognitive impairment and elderly mortality: differences between two cohorts ascertained 6-years apart in China. BMC Geriatr. 2020;20(1):29. 10.1186/s12877-020-1424-4 . Jia L, Du Y, Chu L, et al. Prevalence, risk factors, and management of dementia and mild cognitive impairment in adults aged 60 years or older in China: a cross-sectional study. Lancet Public Health. 2020;5(12):e661–71. 10.1016/S2468-2667(20)30185-7 . Gauthier S, Reisberg B, Zaudig M, et al. Mild cognitive impairment. Lancet. 2006;367(9518):1262–70. 10.1016/S0140-6736(06)68542-5 . Wei B, Dong Q, Ma J, et al. The association between triglyceride-glucose index and cognitive function in nondiabetic elderly: NHANES 2011–2014. Lipids Health Dis. 2023;22(1):188. 10.1186/s12944-023-01959-0 . De Felice FG, Lourenco MV. Brain metabolic stress and neuroinflammation at the basis of cognitive impairment in Alzheimer's disease. Front Aging Neurosci. 2015;7:94. 10.3389/fnagi.2015.00094 . Talbot K, Wang HY, Kazi H, et al. Demonstrated brain insulin resistance in Alzheimer's disease patients is associated with IGF-1 resistance, IRS-1 dysregulation, and cognitive decline. J Clin Invest. 2012;122(4):1316–38. 10.1172/JCI59903 . Sabina M, Alsamman MM. Pulse of Progress: A Systematic Review of Glucagon-Like Peptide-1 Receptor Agonists in Cardiovascular Health. Cardiol Res. 2024;15(1):1–11. 10.14740/cr1600 . Baranowska-Bik A, Bik W. Insulin and brain aging. Prz Menopauzalny. 2017;16(2):44–6. 10.5114/pm.2017.68590 . Ninomiya T. Epidemiological Evidence of the Relationship Between Diabetes and Dementia. Adv Exp Med Biol. 2019;1128:13–25. 10.1007/978-981-13-3540-2_2 . Schrijvers EM, Witteman JC, Sijbrands EJ, et al. Insulin metabolism and the risk of Alzheimer disease: the Rotterdam Study. Neurology. 2010;75(22):1982–7. 10.1212/WNL.0b013e3181ffe4f6 . Hooshmand B, Rusanen M, Ngandu T, et al. Serum Insulin and Cognitive Performance in Older Adults: A Longitudinal Study. Am J Med. 2019;132(3):367–73. 10.1016/j.amjmed.2018.11.013 . Ennis GE, Koscik RL, Ma Y, Jonaitis EM, Van Hulle CA, Betthauser TJ, Randall AM, Chin N, Engelman CD, Anderson R, Suridjan I, Kollmorgen G, et al. Insulin resistance is related to cognitive decline but not change in CSF biomarkers of Alzheimer's disease in non-demented adults. Alzheimers Dement (Amst). 2021;13(1):e12220. 10.1002/dad2.12220 . Fessel J. Cure of Alzheimer's Dementia in Many Patients by Using Intranasal Insulin to Augment an Inadequate Counter-Reaction, Edaravone to Scavenge ROS, and 1 or 2 Other Drugs to Address Affected Brain Cells. J Clin Med. 2023;12(9):3151. 10.3390/jcm12093151 . Euser SM, Sattar N, Witteman JC, et al. A prospective analysis of elevated fasting glucose levels and cognitive function in older people: results from PROSPER and the Rotterdam Study. Diabetes. 2010;59(7):1601–7. 10.2337/db09-0568 . Cohen E, Dillin A. The insulin paradox: aging, proteotoxicity and neurodegeneration. Nat Rev Neurosci. 2008;9(10):759–67. 10.1038/nrn2474 . Koshiyama H. Explanation of the insulin paradox from the evolutionary point of view. Jpn Clin Med. 2012;3:21–4. 10.4137/JCM.S10274 . Zeng Y, Feng Q, Hesketh T, et al. Survival, disabilities in activities of daily living, and physical and cognitive functioning among the oldest-old in China: a cohort study. Lancet. 2017;389(10079):1619–29. 10.1016/S0140-6736(17)30548-2 . Liu J, Zhu X, Liu Y, et al. Association between triglyceride glucose index and suicide attempts in patients with first-episode drug-naïve major depressive disorder. Front Psychiatry. 2023;14:1231524. 10.3389/fpsyt.2023.1231524 . Liu J, Jia F, Li C, et al. Association between body mass index and suicide attempts in Chinese patients of a hospital in Shanxi district with first-episode drug-naïve major depressive disorder. J Affect Disord. 2023;339:377–83. 10.1016/j.jad.2023.06.064 . Lee SH, Park SY, Choi CS. Insulin Resistance: From Mechanisms to Therapeutic Strategies. Diabetes Metab J. 2022;46(1):15–37. 10.4093/dmj.2021.0280 . Wang M, Tan Y, Shi Y, et al. Diabetes and Sarcopenic Obesity: Pathogenesis, Diagnosis, and Treatments. Front Endocrinol (Lausanne). 2020;11:568. 10.3389/fendo.2020.00568 . Versace S, Pellitteri G, Sperotto R, et al. A State-of-Art Review of the Vicious Circle of Sleep Disorders, Diabetes and Neurodegeneration Involving Metabolism and Microbiota Alterations. Int J Mol Sci. 2023;24(13):10615. 10.3390/ijms241310615 . Minh HV, Tien HA, Sinh CT, et al. Assessment of preferred methods to measure insulin resistance in Asian patients with hypertension. J Clin Hypertens (Greenwich). 2021;23(3):529–37. 10.1111/jch.14155 . Caiati C, Stanca A, Lepera ME. Free Radicals and Obesity-Related Chronic Inflammation Contrasted by Antioxidants: A New Perspective in Coronary Artery Disease. Metabolites. 2023;13(6):712. 10.3390/metabo13060712 . Downer B, Kumar A, Mehta H, et al. The Effect of Undiagnosed Diabetes on the Association Between Self-Reported Diabetes and Cognitive Impairment Among Older Mexican Adults. Am J Alzheimers Dis Other Demen. 2016;31(7):564–9. 10.1177/1533317516653824 . Wang X, Chen Q, Liu Y, et al. Causal relationship between multiparameter brain MRI phenotypes and age: evidence from Mendelian randomization. Brain Commun. 2024;6(2):fcae077. 10.1093/braincomms/fcae077 . Taylor EN, Huang N, Wisco J, et al. The brains of aged mice are characterized by altered tissue diffusion properties and cerebral microbleeds. J Transl Med. 2020;18(1):277. 10.1186/s12967-020-02441-6 . Gul A, Sehar M, Jawad U. Relationship between Neuropsychological Functioning, Behavioral Inhibition, and Glycemic Control in Type 2 Diabetes: Findings from Task-switching Study. Chin Med J (Engl). 2017;130(4):487–9. 10.4103/0366-6999.199842 . Smith PP, Kuchel GA, Griffiths D. Functional Brain Imaging and the Neural Basis for Voiding Dysfunction in Older Adults. Clin Geriatr Med. 2015;31(4):549–65. 10.1016/j.cger.2015.06.010 . Nordström CH, Forsse A, Jakobsen RP, et al. Bedside interpretation of cerebral energy metabolism utilizing microdialysis in neurosurgical and general intensive care. Front Neurol. 2022;13:968288. 10.3389/fneur.2022.968288 . Dimitriadis GD, Maratou E, Kountouri A, et al. Regulation of Postabsorptive and Postprandial Glucose Metabolism by Insulin-Dependent and Insulin-Independent Mechanisms: An Integrative Approach. Nutrients. 2021;13(1):159. 10.3390/nu13010159 . Vinson JA, Chen X, Garver DD. Determination of Total Chlorogenic Acids in Commercial Green Coffee Extracts. J Med Food. 2019;22(3):314–20. 10.1089/jmf.2018.0039 . Mohseni S. Hypoglycemic neuropathy. Acta Neuropathol. 2001;102(5):413–21. 10.1007/s004010100459 . Takahashi N, Sakurai T, Davis KL, et al. Linking oligodendrocyte and myelin dysfunction to neurocircuitry abnormalities in schizophrenia. Prog Neurobiol. 2011;93(1):13–24. 10.1016/j.pneurobio.2010.09.004 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7178589","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501603331,"identity":"2667fb65-b214-4e35-8491-43f5cf54c38a","order_by":0,"name":"Diwen Shen","email":"","orcid":"","institution":"Nanjing Drum Tower Hospital, Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Diwen","middleName":"","lastName":"Shen","suffix":""},{"id":501603332,"identity":"c3d084aa-17d7-4552-bada-6556c7404559","order_by":1,"name":"Qi Qian","email":"","orcid":"","institution":"The Second Affiliated Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Qian","suffix":""},{"id":501603333,"identity":"a6cea5e5-f060-40f9-9cc6-17d7bbe942ba","order_by":2,"name":"Yang Liu","email":"","orcid":"","institution":"Nanjing Meishan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Liu","suffix":""},{"id":501603334,"identity":"0a813e93-ff5c-4dee-a11b-fbae6d6ba99f","order_by":3,"name":"Hailong Yang","email":"","orcid":"","institution":"Nanjing Drum Tower Hospital, Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Hailong","middleName":"","lastName":"Yang","suffix":""},{"id":501603335,"identity":"3d03241d-0b20-4f69-9442-b6d182d76953","order_by":4,"name":"Junjun Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYBACefb+5z8+/GCT4ydai2HPGQbJmT18xpINROu5kcMgzcMml7jhALE6GBtyDxjw8JgZGx9P3sDwo2IbYS3sDOcSEiQs0uTMzjwrYOw5c5sIWxobDIDWHDM2u5FjwMzYRoQWhsMMhg0JbP8TN88gWssxHmOGA2xsiRskiNVi2MOWxtjYw2YsAfTLQaL8Ii//+BjzH1BUtidvfPCjghiHIUCCwQGS1IO1kKpjFIyCUTAKRggAAOJYPUQ+4sEGAAAAAElFTkSuQmCC","orcid":"","institution":"Nanjing Meishan Hospital","correspondingAuthor":true,"prefix":"","firstName":"Junjun","middleName":"","lastName":"Liu","suffix":""},{"id":501603336,"identity":"c7604555-fad1-40f6-af03-42c38c02cd11","order_by":5,"name":"Xiangdong Du","email":"","orcid":"","institution":"Suzhou Guangji Hospital, The Affiliated Guangji Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Xiangdong","middleName":"","lastName":"Du","suffix":""}],"badges":[],"createdAt":"2025-07-21 14:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7178589/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7178589/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89666832,"identity":"21cc9e8b-5ef0-4c11-b2c4-4f6d75f5ca66","added_by":"auto","created_at":"2025-08-22 12:08:54","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":164990,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart of this study\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7178589/v1/e52bbee21cf752535852600e.jpg"},{"id":89666833,"identity":"3b4502e8-80d9-45d9-8b6d-84e80dc2197d","added_by":"auto","created_at":"2025-08-22 12:08:54","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":45983,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe relationship between TyG index and the probability of cognitive impairment. \u003c/strong\u003eA nonlinear relationship between TyG index and the probability of cognitive impairment was observed after adjusting for age, sex, marital status, years of education, BMI, current exercising, current smoking, current drinking (P for non-linearity = 0.011).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7178589/v1/d0225df848e27291fd3c7a0f.jpg"},{"id":89667543,"identity":"ac28b72e-4628-46ab-8710-3abea45a6b2d","added_by":"auto","created_at":"2025-08-22 12:16:49","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":84093,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubgroup analysis of the association between TyG index and cognitive impairment.\u003c/strong\u003e The HR (95% CI) was derived from the Cox regression model. (age, sex, marital status, years of education, BMI, current exercising, current smoking, current drinkingwere adjusted, except for the stratified variable itself).\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7178589/v1/cd1c745673c62378305d43d9.jpg"},{"id":105898066,"identity":"ddd7843a-9057-4e3a-89c1-555078e1f1e6","added_by":"auto","created_at":"2026-04-01 08:59:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1502496,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7178589/v1/51c1d6c0-097c-487f-b559-f405271f8614.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"TyG Index and Cognitive Decline in Non-diabetic Elderly: Evidence from CLHLS 2014-2018","fulltext":[{"header":"1. Background","content":"\u003cp\u003eOver the past few decades, life expectancy has significantly increased, thanks to remarkable advancements in modern medicine and improved living conditions, especially in emerging nations. Consequently, the elderly population is projected to experience prolonged longevity in the future. However, this rise in life expectancy has led to an increase in age-related disorders [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In China, this demographic shift poses a pressing public health challenge. According to data from China's most recent national census, by 2021, the population aged 60 and above had grown to 264\u0026nbsp;million, constituting 18.70% of China's total inhabitants. This represents a 5.44% increase compared to the sixth national census, with further growth expected in the coming decades [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. By 2040, projections suggest that elderly individuals will comprise approximately 22.6% of the total population [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The global phenomenon of population aging has brought cognitive impairment into the spotlight as a significant public health concern. With the continuous increase in life expectancy, the aging process accelerates, putting a considerable number of older adults at a higher risk of developing cognitive impairment. This condition, characterized by declines in memory, attention, language, and other cognitive functions, represents an intermediary state between normal aging and dementia [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn the context of a rapidly aging global population, cognitive impairment has emerged as a significant determinant of compromised health [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Its association with reduced life expectancy underscores the substantial burden it places on public health infrastructure. Particularly in China, the prevalence rates of dementia and cognitive decline among individuals aged 60 and above were 6.04% and 15.54%, respectively, in 2020 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Among those over 65 years old, mild cognitive impairment (MCI) affects a range from 3\u0026ndash;19% of the population. Alarmingly, more than half of these individuals progress to dementia within a five-year period following diagnosis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. For older adults, MCI serves as a risk factor for the progression to severe cognitive impairment or dementia, increasing the burden on individuals and caregivers and making independent living challenging. Currently, no definitive treatment for dementia exists, although two anti-amyloid therapies recently received FDA approval, which may slow disease progression [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Consequently, pinpointing the elements that predispose individuals to cognitive decline is essential in efforts to avert dementia in the aging population.\u003c/p\u003e\u003cp\u003eInsulin resistance (IR) has emerged as a key factor in metabolic syndrome, with recent studies demonstrating indicated a strong association between the progression of Alzheimer's disease (AD) and metabolic abnormalities [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. According to the World Health Organization (WHO), disrupted glucose metabolism represents a major global health issue. Projections indicate that obesity and type 2 diabetes mellitus (T2DM) will affect 500\u0026nbsp;million individuals worldwide by 2023, reflecting a substantial increase in these conditions over the past five decades [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. IR, characterized by impaired cellular response to insulin, is closely linked to T2DM. Beyond inducing hyperglycemia, IR is implicated in the development of neurodegenerative disorders later in life, including AD [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Epidemiological studies reveal significant similarities in the risk factors and pathophysiological mechanisms underlying T2DM and AD. IR and hyperglycemia initiate cascades of processes that contribute to neuronal death and cognitive decline [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The majority of research indicates that IR adversely affects cognitive function, with prior studies linking insulin resistance, measured using the Homeostasis Model Assessment (HOMA-IR) method, to increased risk of AD [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], impaired global cognition [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and accelerated cognitive decline [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Increasing epidemiological data demonstrate the usefulness of IR in predicting cognitive impairment. Furthermore, clinical trials have shown that intranasal insulin treatment is beneficial for patients with AD and early cognitive decline [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, conflicting results have emerged from some research examining the link between IR and cognitive impairment. Euser et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], through meticulous examination of data from two distinct prospective studies involving a collective cohort of 8,447 participants, concluded that heightened insulin resistance and fasting glucose levels do not correlate with diminished cognitive function in older individuals without a history of diabetes. It is noteworthy that although a growing number of investigations explore the potential implications of reduced central nervous system (CNS) insulin signaling in the pathogenesis of neurodegenerative disorders, diminished insulin signaling has also demonstrated efficacy in enhancing lifespan regulation and delaying age-related processes. Remarkably, the attenuation of insulin signaling has the capacity to extend lifespan and delay the onset of protein-aggregation-induced harm, despite its potential to precipitate diabetes [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Moreover, an expanding body of literature has elucidated that restraining insulin activity augments life expectancy across diverse organisms, including primates and vertebrates. Hence, the paradoxical nature of insulin's effects appears to be substantiated [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eExtended longitudinal and large-scale cohort studies are necessary to address the inconsistent evidence regarding how IR affects cognitive function over time. The China Longitudinal Healthy Longevity Survey (CLHLS) is a prospective, dynamic cohort consisting of older Chinese individuals residing in communities. Since its establishment in 1998, the CLHLS has been regularly monitored every 2\u0026ndash;3 years. Therefore, utilizing CLHLS data to explore IR's influence on cognitive decline among non-diabetic elderly population, our research seeks to contribute population-based insights into the health implications of IR. Few longitudinal investigations have been conducted to date on the connection between IR and] cognitive impairment in older Chinese adults. Hence, using the longitudinal dataset from the CLHLS, covering the years 2014 to 2018, our investigation aims to assess the relationship between cognitive decline and an alternative measure of IR: the triglyceride glucose (TyG) index.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Participants and study framework\u003c/h2\u003e\u003cp\u003eOur research utilized data from the CLHLS, an ongoing community-based longitudinal investigation that has been going on since 1998. Employing a targeted, disproportionate sampling strategy, the CLHLS aimed to encompass nearly half of China's cities and counties across 23 provinces, thereby ensuring an adequate representation of older adults [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Between 1998 and 2018, eight follow-up surveys were conducted. To mitigate cohort attrition due to mortality and loss of follow-up, new participants were periodically recruited during each survey wave. In order to identify health risk factors among individuals aged 65 and above, particularly those aged 80 and older, the CLHLS's main goal is to gather extensive data on lifestyle, health status, and demography. Follow-ups take place every three to four years. For this study, trained physicians from public health officials and community hospitals from the Centers for Disease Control and Prevention (CDC) conducted in-home, in-person interviews. Previous research has demonstrated that the CLHLS maintains an exceptionally low non-response rate, with an average data attrition rate of 4.85% per wave, comparable to major Western cohorts, and generally upholds acceptable data quality standards.\u003c/p\u003e\u003cp\u003eData from the CLHLS's 2014\u0026ndash;2018 cohort were used in the study. Eligibility criteria required participants to have complete data on glucose and triglyceride levels and to have reached or exceeded 65 years of age by 2014. Exclusion criteria encompassed individuals with diabetes or cognitive impairment at baseline, those lacking Mini-Mental State Examination (MMSE) scores in 2014 or 2018, and individuals who could not be contacted for follow-up or had decreased by 2018. Ultimately, the study cohort consisted of 988 individuals. The enrollment and follow-up procedures are detailed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e The CLHLS was granted ethical approval by the institutional review boards at both Duke University and Peking University (with approval number IRB00001052\u0026ndash;13074), in accordance with the Declaration of Helsinki. All participants provided informed consent when time of their enrollment. Interviewers, trained by the Chinese CDC, conducted in-home interviews using a standardized questionnaire to ensure the accurate collection of detailed participant data and to provide attentive care for the elderly. The questionnaire is accessible on the internet at the Peking University Open Research Data platform: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://opendata.pku.edu.cn/\u003c/span\u003e\u003cspan address=\"https://opendata.pku.edu.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Data collection and variable measures\u003c/h2\u003e\u003cp\u003eUnder rigorous quality control protocols, face-to-face questionnaire surveys were employed to collect sociodemographic characteristics, health behaviors, and comorbid conditions during the 2014 baseline survey. Sociodemographic factors included age, sex, educational attainment (0, 1\u0026ndash;6, and \u0026ge;\u0026thinsp;7 years of schooling), and marital status (married, single, divorced, or widowed). The health behaviors assessed encompassed current exercise routines, smoking status, and alcohol consumption. The comorbid conditions recorded included hypertension, diabetes, cancer, heart disease, stroke, and other cardiovascular diseases.\u003c/p\u003e\u003cp\u003eCognitive performance during each survey was evaluated utilizing a validated cognitive assessment instrument, the Mini-Mental State Examination (MMSE). This tool was previously validated through pilot survey interviews. To better accommodate the elderly population in China, several items in the Chinese version of the MMSE were cross-culturally adapted, with their validity and reliability rigorously confirmed [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The MMSE comprises six domains: orientation, registration, attention, language, memory, and visuoconstructional skills, with total scores ranging from 0 to 30. Cognitive impairment was categorized based on educational attainment: scores below 17 indicated illiteracy, scores below 20 corresponded to one to six years of education, and scores below 24 were indicative of seven or more years of education. The study exclusively enrolled participants with normal baseline cognitive function to assess cognitive impairment. The endpoint for follow-up was defined as the onset of cognitive impairment in subjects. The time span was determined by calculating the follow-up period, marking the period over which cognitive changes were monitored, which began with the 2014 baseline survey and ended on the most current follow-up date.\u003c/p\u003e\u003cp\u003eBlood pressure measurements were obtained using a standard mercury sphygmomanometer after at least 15 minutes of seated rest. The right arm's blood pressure was measured twice, and the average of these readings was used to determine systolic and diastolic blood pressures (SBP and DBP, respectively) by averaging two readings. The body mass index (BMI) was calculated based on conventional anthropometric measurements provided by each patient, with the formula BMI\u0026thinsp;=\u0026thinsp;weight/(height)^2, where height is in meters (m) and weight in kilograms (kg). Following an overnight fast, venous blood samples were drawn and used to measure blood biomarkers such as lipid profile components (triglycerides, total cholesterol, high-density lipoprotein cholesterol) and fasting blood glucose. Using the logarithmic transformation of TG and FBG, the TyG index [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] was derived using a specific formula: TyG\u0026thinsp;=\u0026thinsp;Ln [FBG (mg/dL) \u0026times; TG (mg/dL)/2].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Statistical analysis\u003c/h2\u003e\u003cp\u003eTo assess the normality of all continuous variables, the researchers employed the one-sample Kolmogorov-Smirnov test. To analyze the differences among the various groups, a one-way ANOVA was applied for continuous variables, while categorical variables were assessed using a chi-squared test. To investigate the association between the TyG index and cognitive decline, we employed Cox regression models. For sensitivity analysis, the TyG index was categorized into tertiles. To evaluate potential multicollinearity among independent variables, we calculated the variance inflation factor (VIF). Any variables with a VIF exceeding 5 were deemed to exhibit multicollinearity and were subsequently removed from the final Cox regression analysis. For the final multivariable model, potential confounders were included if they influenced the TyG index estimates related to cognitive impairment by more than 10% or had a significance level below 0.10 in univariable analysis [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In cases where the TyG index and cognitive impairment showed a non-linear association, we utilized a smoothing plot and a two-piecewise linear regression model, implemented through the Generalized Estimating Equation (GEE) approach.\u003c/p\u003e\u003cp\u003eThe EmpowerStats (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.empowerstats.com\u003c/span\u003e\u003cspan address=\"http://www.empowerstats.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, X\u0026amp;Y Solutions, Inc., Boston, Massachusetts, USA) and R software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.r-project.org\u003c/span\u003e\u003cspan address=\"http://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, The R Foundation) were employed to conduct all statistical analyses. A two-tailed test was used to assess statistical significance, with the threshold set at a p-value less than 0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eFrom the CLHLS cohort that ran from 2014 to 2018, a subset of 988 participants was selected. These individuals, among the initial 7192 participants, had complete TyG index data and a MMSE score of 24 or above in 2014 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Participants were excluded if their TyG index data were incomplete (n\u0026thinsp;=\u0026thinsp;4963), they were lost to follow-up (n\u0026thinsp;=\u0026thinsp;327), died in 2018 (n\u0026thinsp;=\u0026thinsp;813), were younger than 65 years (n\u0026thinsp;=\u0026thinsp;69), lacked MMSE scores in 2014 (n\u0026thinsp;=\u0026thinsp;115), had cognitive impairment in 2014 (n\u0026thinsp;=\u0026thinsp;79), or lacked total MMSE scores in 2018 (n\u0026thinsp;=\u0026thinsp;108).\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Participants' demographic and clinical characteristics\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays the initial characteristics of the study participants, categorized by TyG index tertiles. The study cohort included 988 individuals, whose mean age was 79.78\u0026thinsp;\u0026plusmn;\u0026thinsp;9.21 years, with males comprising 572 (57.89%) of the sample. The TyG index tertiles were found to be significantly correlated with a number of variables (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), including age, gender, BMI, SBP, DBP, current drinking and smoking status, prevalence of heart disease and hypertension, FBG, TG, TC, HDL-c, and MMSE total scores in 2014. In contrast, no significant associations were found among TyG index tertiles and factors such as years of education, marital status, current exercise habits, history of stroke or cardiovascular disease, cancer, and follow-up duration (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTyG index tertile\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eT1 (6.24\u0026ndash;8.15)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT2 (8.16\u0026ndash;8.65)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eT3 (8.66\u0026ndash;11.23)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e325\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e325\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e338\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79.78\u0026thinsp;\u0026plusmn;\u0026thinsp;9.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80.87\u0026thinsp;\u0026plusmn;\u0026thinsp;9.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80.24\u0026thinsp;\u0026plusmn;\u0026thinsp;9.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e78.30\u0026thinsp;\u0026plusmn;\u0026thinsp;8.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e572 (57.89%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e222 (68.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e189 (58.15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e161 (47.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e416 (42.11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e103 (31.69%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e136 (41.85%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e177 (52.37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYears of education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.556\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e459 (47.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e137 (43.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e155 (48.74%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e167 (49.85%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e374 (38.68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e131 (41.72%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e118 (37.11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e125 (37.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;=7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e134 (13.86%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46 (14.65%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45 (14.15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43 (12.84%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.953\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e558 (57.88%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e181 (57.46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e182 (57.59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e195 (58.56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle, divorced, or widowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e406 (42.12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e134 (42.54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e134 (42.41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e138 (41.44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent exercising\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.326\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e772 (79.67%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e247 (78.16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e250 (78.37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e275 (82.34%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e197 (20.33%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69 (21.84%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69 (21.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e59 (17.66%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e790 (80.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e245 (76.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e260 (80.75%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e285 (84.57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e191 (19.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77 (23.91%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62 (19.25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52 (15.43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent drinking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e797 (81.49%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e249 (77.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e266 (83.12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e282 (84.18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e181 (18.51%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74 (22.91%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54 (16.88%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e53 (15.82%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e667 (69.12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e233 (74.44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e219 (68.65%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e215 (64.56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e298 (30.88%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80 (25.56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100 (31.35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e118 (35.44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e884 (91.13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e299 (94.92%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e294 (91.59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e291 (87.13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e86 (8.87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (5.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 (8.41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43 (12.87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStroke or cvd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.141\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e907 (93.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e302 (95.57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e296 (91.93%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e309 (92.51%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65 (6.69%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (4.43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26 (8.07%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25 (7.49%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.557\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e930 (99.25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e305 (99.03%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e313 (99.68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e312 (99.05%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (0.75%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (0.97%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (0.32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (0.95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.42\u0026thinsp;\u0026plusmn;\u0026thinsp;3.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.43\u0026thinsp;\u0026plusmn;\u0026thinsp;3.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.21\u0026thinsp;\u0026plusmn;\u0026thinsp;4.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23.56\u0026thinsp;\u0026plusmn;\u0026thinsp;3.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e143.41\u0026thinsp;\u0026plusmn;\u0026thinsp;22.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e139.86\u0026thinsp;\u0026plusmn;\u0026thinsp;23.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e144.91\u0026thinsp;\u0026plusmn;\u0026thinsp;22.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e145.38\u0026thinsp;\u0026plusmn;\u0026thinsp;20.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81.06\u0026thinsp;\u0026plusmn;\u0026thinsp;12.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79.70\u0026thinsp;\u0026plusmn;\u0026thinsp;13.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82.15\u0026thinsp;\u0026plusmn;\u0026thinsp;13.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e81.34\u0026thinsp;\u0026plusmn;\u0026thinsp;10.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFBG (mmol/l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.36\u0026thinsp;\u0026plusmn;\u0026thinsp;2.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC (mmol/l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG (mmol/l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-c (mmol/l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMMSE total score in 2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFollow up time (months)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41.64\u0026thinsp;\u0026plusmn;\u0026thinsp;2.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41.81\u0026thinsp;\u0026plusmn;\u0026thinsp;2.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41.48\u0026thinsp;\u0026plusmn;\u0026thinsp;2.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41.64\u0026thinsp;\u0026plusmn;\u0026thinsp;2.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.302\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: The variables are presented as n (%) or the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, TyG: triglyceride glucose; BMI: body mass index; FBG: fasting blood glucose; TC: total cholesterol; TG: triglyceride; HDL-c: high-density lipoprotein cholesterol; SBP: systolic blood pressure; DBP: diastolic blood pressure; MMSE: Mini-Mental State Examination.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Univariate analysis of cognitive impairment\u003c/h2\u003e\u003cp\u003eThroughout the observational period spanning 2014 to 2018, cognitive impairment manifested in 201 individuals (20.3%) within the cohort, as recorded over a four-year follow-up duration. The outcomes of our univariate statistical assessment are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Age (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.08, 95% \u003cem\u003eCI\u003c/em\u003e: 1.06\u0026ndash;1.09), gender (female) (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.84, 95% \u003cem\u003eCI\u003c/em\u003e: 1.39\u0026ndash;2.44 versus male), and marital status (single, divorced, or widowed) (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.65, 95% \u003cem\u003eCI\u003c/em\u003e: 1.96\u0026ndash;3.57 versus married) were risk factors that showed positive correlations with cognitive impairment; all parameters were statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, protective elements linked to decreased incidence of cognitive impairment encompassed 1\u0026ndash;6 years of education (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.30, 95% \u003cem\u003eCI\u003c/em\u003e: 0.21\u0026ndash;0.43 versus 0 years), exceeding 7 years of education (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.15, 95% \u003cem\u003eCI\u003c/em\u003e: 0.07\u0026ndash;0.32 versus 0 years), habitual physical activity (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.59, 95% CI: 0.39\u0026ndash;0.88), current tobacco use (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.43, 95% \u003cem\u003eCI\u003c/em\u003e: 0.27\u0026ndash;0.69), continuing alcohol consumption (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.45, 95% \u003cem\u003eCI\u003c/em\u003e: 0.30\u0026ndash;0.75), body mass index (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.93, 95% \u003cem\u003eCI\u003c/em\u003e: 0.89\u0026ndash;0.98), total cholesterol (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.78, 95% \u003cem\u003eCI\u003c/em\u003e: 0.63\u0026ndash;0.96), TyG index (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.79, 95% \u003cem\u003eCI\u003c/em\u003e: 0.63\u0026ndash;0.99), tertiles categorized as T3 for TyG index (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.63, 95% \u003cem\u003eCI\u003c/em\u003e: 0.45\u0026ndash;0.89 versus T1), and MMSE total score in 2014 (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.74, 95% \u003cem\u003eCI\u003c/em\u003e: 0.69\u0026ndash;0.79) (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Nevertheless, variables including hypertension, heart disease, history of stroke or cardiovascular disease, cancer, SBP, DBP, FBG, triglycerides, HDL-c, and tertiles classified as T2 for TyG index (versus T1) did not exhibit significant associations with cognitive impairment (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate analysis for cognitive impairment\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCovariate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStatistics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79.78\u0026thinsp;\u0026plusmn;\u0026thinsp;9.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.08 (1.06, 1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e572 (57.89%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0 (reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e416 (42.11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.84 (1.39, 2.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYears of education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e459 (47.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0 (reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e374 (38.68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.30 (0.21, 0.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;=7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e134 (13.86%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.15 (0.07, 0.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e558 (57.88%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0 (reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle, divorced, or widowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e406 (42.12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.65 (1.96, 3.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent exercising\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e772 (79.67%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0 (reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e197 (20.33%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.59 (0.39, 0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e790 (80.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0 (reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e191 (19.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.43 (0.27, 0.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent drinking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e797 (81.49%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0 (reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e181 (18.51%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.45 (0.30, 0.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e667 (69.12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0 (reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e298 (30.88%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.11 (0.82, 1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.490\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e884 (91.13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0 (reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e86 (8.87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.05 (0.63, 1.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.853\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStroke or CVD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e907 (93.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0 (reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65 (6.69%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.37 (0.78, 2.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.274\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e930 (99.25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0 (reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (0.75%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00 (0.00, Inf)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.993\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.42\u0026thinsp;\u0026plusmn;\u0026thinsp;3.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.93 (0.89, 0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e143.41\u0026thinsp;\u0026plusmn;\u0026thinsp;22.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.99 (0.99, 1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81.06\u0026thinsp;\u0026plusmn;\u0026thinsp;12.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00 (0.98, 1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.451\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFBG (mmol/l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.98 (0.89, 1.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.589\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC (mmol/l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.78 (0.63, 0.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG (mmol/l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.88 (0.76, 1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-c (mmol/l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.91 (0.63, 1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.627\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.79 (0.63, 0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG index tertiles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e325 (32.89%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0 (reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e325 (32.89%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.80 (0.57, 1.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.176\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e338 (34.21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.63 (0.45, 0.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMMSE total score in 2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.74 (0.69, 0.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: The variables are presented as n (%) or the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, HR: Hazard Ratio; CI: confidence interval; TyG: triglyceride glucose; BMI: body mass index; FBG: fasting blood glucose; TC: total cholesterol; TG: triglyceride; HDL-c, high-density lipoprotein cholesterol; SBP: systolic blood pressure; DBP: diastolic blood pressure; MMSE: Mini-Mental State Examination.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Relationships between cognitive impairment and TyG index\u003c/h2\u003e\u003cp\u003eInitially, TyG index was assessed as a continuous measure. An inverse correlation emerged between the TyG index and cognitive impairment (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in unadjusted regression analyses. However, in both partially and fully adjusted models, this correlation lost its statistical significance (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). For further analysis, we stratified into tertiles for further examination. Notably, participants in the highest tertile demonstrated a reduced likelihood of cognitive decline compared to those in the lowest tertile after adjustment for multivariate covariates, yielding a hazard ratio of 0.60 (95% \u003cem\u003eCI\u003c/em\u003e: 0.38\u0026ndash;0.94, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027). The findings suggest a potential curvilinear association between TyG index levels and cognitive impairment, as elaborated in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRelationship between TyG index and cognitive impairment in different models\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eUnadjusted Model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eModel Ⅰ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003eModel Ⅱ\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.79 (0.63, 0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.80 (0.59, 3.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.77 (0.56, 1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG index tertile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1 (6.24\u0026ndash;8.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e325\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0 (reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.0 (reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.0 (reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2 (8.16\u0026ndash;8.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e325\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.80 (0.57, 1.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.75 (0.49, 1.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.78 (0.51, 1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.254\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3 (8.66\u0026ndash;11.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e338\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.63 (0.45, 0.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.65 (0.42, 1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.60 (0.38, 0.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eAbbreviations: HR, hazard ratio; CI, confidence interval; Unadjusted Model adjusted for none; Model Ⅰ adjusted for age, sex, marital status, and years of education; Model Ⅱ adjusted for age, sex, marital status, years of education, BMI, current exercising, current smoking, current drinking.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Analyses of the non-linear relationship\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, derived from generalized additive models, illustrates a curvilinear relationship linking TyG index levels to cognitive impairment (P for non-linearity\u0026thinsp;=\u0026thinsp;0.011). An inflection point was found with a TyG index value of 7.57 using a segmented Cox regression analysis with two components. After this critical threshold, every one-unit elevation in the TyG index correlates with a significant 33% decrease in cognitive impairment likelihood (HR\u0026thinsp;=\u0026thinsp;0.67, 95% CI: 0.50 to 0.91, P\u0026thinsp;=\u0026thinsp;0.009). For values beneath the inflection point, the analysis revealed no statistically meaningful association (HR\u0026thinsp;=\u0026thinsp;64.39, 95% CI: 0.44 to 9367.71, P\u0026thinsp;=\u0026thinsp;0.101). Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates this difference. The inflection point confidence intervals (7.54 to 7.86) were estimated using bootstrapping techniques. Among the participants, 935 had a TyG index of 7.57 or higher, whereas only 53 had an index below this threshold. The small sample size for cases with a TyG index below 7.57 (n\u0026thinsp;=\u0026thinsp;53) limits the statistical robustness of these findings, necessitating cautious interpretation of the results regarding cognitive impairment in this subgroup.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe results of two-piecewise Cox regression model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInflection points of TyG index\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInflection point\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.54 to 7.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;7.57 slope 1 (n\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.44 to 9367.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;= 7.57 slope 2 (n\u0026thinsp;=\u0026thinsp;935)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.50 to 0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eslope 2 \u0026ndash; slope 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00 to 1.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog likelihood ratio test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eEffect: cognitive impairment in 2018; Cause: TyG index; adjusted for age, sex, marital status, years of education, BMI, current exercising, current smoking, current drinking.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eSlope 1 and slope 2 are the slope coefficients for the segment before and after the inflection point.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Subgroup analyses of cognitive impairment and TyG index\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e showcases a subgroup analysis indicating a consistent pattern across diverse demographic and lifestyle variables, including gender (male, female), age (\u0026lt;\u0026thinsp;80, 80\u0026ndash;89, 90\u0026ndash;99, \u0026ge;\u0026thinsp;100), marital status (married, single, divorced, widowed), and current habits of exercising, smoking, and drinking. Each subgroup analysis yielded a \u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05, suggesting no significant interaction effects.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study marks the inaugural cohort investigation in China examining the association between the TyG index and cognitive decline in elderly non-diabetic individuals, providing several novel insights. After adjusting for covariates, the Cox regression model demonstrated a 40% reduction in cognitive impairment likelihood for participants in the upper tertile (T3) relative to those in the lowest (T1) when stratifying the TyG index into tertiles. Furthermore, our findings revealed a curvilinear association linking TyG index values to cognitive impairment risk, with a notable turning point at 7.57. For TyG values below this threshold, the relationship was statistically insignificant, while to the right, it became distinctly negative. These findings highlight the complex nature of the dynamics between IR and the risk of cognitive impairment within this population, providing valuable implications for public health strategies and policy development.\u003c/p\u003e\u003cp\u003eInsulin resistance is characterized by impaired glucose oxidation and uptake in response to insulin, decreased glycogen synthesis, and a reduced ability to inhibit lipid oxidation in peripheral tissues, including fat deposits, hepatic cells, and muscle fibers [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. To compensate for the decreased effectiveness of insulin, the body secretes excessive amounts of insulin, resulting in hyperinsulinemia [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This disruption in energy homeostasis correlates with alterations in genetic activity and protein production, which have been linked to elevated levels of triglycerides, the formation of reactive oxygen species (ROS), the secretion of inflammatory mediators, and increased lipolysis [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Intravenous glucose tolerance tests and euglycemic insulin clamps are considered the gold standard for diagnosing insulin resistance; however, their high cost and invasive nature limit their use in clinical practice [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Consequently, the TyG index, which measures serum triglycerides and fasting blood glucose, has been proposed as a cost-effective and user-friendly indicator of insulin resistance (IR) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Existing studies have demonstrated a connection linking IR to cognitive deterioration. Systemic IR and its related metabolic disturbances may elevate the likelihood of oxidative damage, cerebrovascular disorders, amyloid β accumulation in the brain, and increased concentrations of inflammatory mediators, such as interleukin-6 and tumor necrosis factor-alpha, which can lead to neurodegeneration [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Furthermore, hyperinsulinemia may impair insulin levels in the cerebrospinal fluid and disrupt brain insulin signaling pathways, leading to reduced mitochondrial activity and abnormal neurosynaptic function [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. These alterations ultimately have the potential to compromise cognitive performance.\u003c/p\u003e\u003cp\u003eHowever, the findings from this cohort study suggest that, among a non-diabetic Chinese population, higher baseline levels of insulin resistance are correlated with reduced incidence of cognitive decline over a 4-year period. This finding raises the possibility of an \"insulin resistance paradox\" in the relationship between insulin resistance in older individuals and cognitive impairment. Specifically, the correlation becomes statistically significant for TyG index values above 7.57. A single-unit rise in the TyG index corresponds to a 33% reduction in the probability of cognitive impairment over the four-year timeframe. The exact pathological mechanisms underlying this inverse relationship remain to be fully understood. Aging poses a significant threat to brain health, primarily through the degradation of white matter, which is manifested as abnormal hyperintensity, compromised white matter integrity around the hippocampus, and cortical atrophy, particularly in the frontal lobes [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The brain regions associated with memory and cognition show substantial age-related reductions, with the most significant changes observed in total brain volume, frontal lobe volume, and medial temporal lobe volume. In the later stages of disease, additional neocortical cell loss occurs [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This leads to clinical and anatomical abnormalities, including overall brain shrinkage, increased cortical thinning in the frontal lobe, ventricular enlargement, increased white matter ischemia, and reduced hippocampal volume [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In elderly individuals, cerebral blood flow gradually decreases, which adversely affects the brain's supply of oxygen and nutrients, thus exacerbating cognitive decline [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Glucose metabolism is the sole energy source for brain tissue, with increased neuroactivity further increasing energy demands [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Insulin resistance in skeletal muscle facilitates the redirection of glucose from peripheral tissues to the brain, which is more energy-dependent and vulnerable compared to other tissues [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Under adverse conditions, these mechanisms may help conserve glucose for cerebral use. Lower insulin resistance has been associated with lower fasting blood glucose levels, waist circumference (WC), body weight, BMI, TC, TG, and LDL-c [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Prolonged hypoglycemia can deprive neurons of essential oxygen and nutrients, potentially leading to neuronal damage. Extended periods of low blood sugar may cause pathological changes in the nervous system, including myelin degradation, synapse loss, and neuronal atrophy [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Considering the known link between lipid metabolism and cognitive decline, it is noteworthy that the brain contains approximately 20% of the body's cholesterol, with myelin-producing oligodendrocytes responsible for about 70% of this amount. These myelinating oligodendrocytes are crucial for multiple neuronal functions, such as enhancing the velocity of information processing, resilience to oxidative stress, and the maintenance of blood-brain barrier integrity [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Given the complex relationship between cognitive impairment and the TyG index, further comprehensive investigations are needed to elucidate the underlying pathological mechanisms.\u003c/p\u003e\u003cp\u003eThis study has several strengths that address limitations observed in previous research. Specifically, we utilized a large, nationally representative Chinese elderly cohort with an extended follow-up period, providing more robust longitudinal evidence on the relationship between cognitive function and the TyG index among elderly individuals without diabetes. Nevertheless, it's crucial to recognize certain constraints in our research design. Firstly, the restricted availability of biological sample data for the oldest individuals in the CLHLS database resulted in a small sample size for the elderly population studied. Secondly, the study population consisted only of elderly Chinese participants, which may restrict the applicability of the results to the broader Chinese population and other nations or ethnic groups. Thirdly, this study did not evaluate alternative measures of insulin resistance such as homeostasis model assessment of insulin resistance (HOMA-IR) or the revised quantitative insulin sensitivity check index (QUICKI), which are commonly used surrogate measures derived from fasting insulin and glucose data. Fourthly, cognitive function was assessed using the MMSE, which is a validated tool for population-based studies but not a professional diagnostic tool for cognitive impairment. Fifth, we did not examine whether changes in the TyG index throughout the study period correlated with heightened likelihood of cognitive impairment, as our analysis was limited to baseline levels. Sixth, the study population consisted of non-diabetic elderly individuals from China, whose average age was approximately 80 years and an average TyG index of 8.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62. Therefore, additional studies are required to assess whether these results apply to different demographic groups. Lastly, potential unidentified or unquantified variables might affect the observed association between cognitive impairment and the TyG index. However, we adjusted for common confounders in various models, and the robustness of the results remained.\u003c/p\u003e\u003cp\u003eIn conclusion, this nationwide retrospective cohort study revealed a curvilinear relationship linking the TyG index with cognitive impairment in non-diabetic elderly individuals in China, with a negative correlation observed above a TyG index threshold of 7.57. These findings provide some support for the \"insulin resistance paradox\" theory. Monitoring and controlling the TyG index may potentially enhance cognitive performance in older individuals without diabetes. Nevertheless, further rigorous multicenter studies are required to confirm these results and investigate the underlying mechanisms, which could aid in risk assessment and primary prevention of cognitive decline in the elderly.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original data used in this paper were derived from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). The data are available from the website: https://opendata.pku.edu.cn/dataverse/CHADS. Processed data described in the manuscript, code book, and analytic code will be made available upon request pending an approval from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy Design: Xiangdong Du, Junjun Liu. Investigation: Diwen Shen, Qi Qian, Yang Liu, Hailong Yang. Analysis and interpretation of data: Junjun Liu, Diwen Shen, Qi Qian. Drafting of the manuscript: Junjun Liu, Hailong Yang. Critical revision of the manuscript: Xiangdong Du. Approval of the final version for publication: Junjun Liu, Xiangdong Du, Diwen Shen.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their gratitude to the participants and staff involved in the data collection and management in the Chinese Longitudinal Healthy Longevity Survey (CLHLS).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Medical Science and Technology Development Foundation, Nanjing Department of Health (Nos. YKK21216, YKK20184, YKK22264). The funding sources of this study had no role in study design, data collection and analysis, decision to publish, or preparation of the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent for publication was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Peking University biomedical ethics commission (IRB0000105224713074) authorized all procedures involving research study participants in the Chinese Longitudinal Healthy Longevity Survey (CLHLS), which is openly accessible, in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJin K, Simpkins JW, Ji X, et al. The Critical Need to Promote Research of Aging and Aging-related Diseases to Improve Health and Longevity of the Elderly Population. Aging Dis. 2014;6(1):1\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.14336/AD.2014.1210\u003c/span\u003e\u003cspan address=\"10.14336/AD.2014.1210\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang J, Yang Z, Li Y, et al. Status and influencing factors of elder neglect by geriatric nursing assistants in Chinese nursing homes: a cross-sectional survey. Front Med (Lausanne). 2023;10:1273289. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmed.2023.1273289\u003c/span\u003e\u003cspan address=\"10.3389/fmed.2023.1273289\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu X, Luo Z, Tian G, et al. Hypotension and cognitive impairment among the elderly: Evidence from the CLHLS. PLoS ONE. 2023;18(9):e0291775. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0291775\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0291775\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRajan KB, Weuve J, Barnes LL, et al. Population estimate of people with clinical Alzheimer's disease and mild cognitive impairment in the United States (2020\u0026ndash;2060). Alzheimers Dement. 2021;17(12):1966\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/alz.12362\u003c/span\u003e\u003cspan address=\"10.1002/alz.12362\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDuan J, Lv YB, Gao X, et al. Association of cognitive impairment and elderly mortality: differences between two cohorts ascertained 6-years apart in China. BMC Geriatr. 2020;20(1):29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12877-020-1424-4\u003c/span\u003e\u003cspan address=\"10.1186/s12877-020-1424-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJia L, Du Y, Chu L, et al. Prevalence, risk factors, and management of dementia and mild cognitive impairment in adults aged 60 years or older in China: a cross-sectional study. Lancet Public Health. 2020;5(12):e661\u0026ndash;71. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S2468-2667(20)30185-7\u003c/span\u003e\u003cspan address=\"10.1016/S2468-2667(20)30185-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGauthier S, Reisberg B, Zaudig M, et al. Mild cognitive impairment. Lancet. 2006;367(9518):1262\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(06)68542-5\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(06)68542-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWei B, Dong Q, Ma J, et al. The association between triglyceride-glucose index and cognitive function in nondiabetic elderly: NHANES 2011\u0026ndash;2014. Lipids Health Dis. 2023;22(1):188. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12944-023-01959-0\u003c/span\u003e\u003cspan address=\"10.1186/s12944-023-01959-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDe Felice FG, Lourenco MV. Brain metabolic stress and neuroinflammation at the basis of cognitive impairment in Alzheimer's disease. Front Aging Neurosci. 2015;7:94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnagi.2015.00094\u003c/span\u003e\u003cspan address=\"10.3389/fnagi.2015.00094\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTalbot K, Wang HY, Kazi H, et al. Demonstrated brain insulin resistance in Alzheimer's disease patients is associated with IGF-1 resistance, IRS-1 dysregulation, and cognitive decline. J Clin Invest. 2012;122(4):1316\u0026ndash;38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1172/JCI59903\u003c/span\u003e\u003cspan address=\"10.1172/JCI59903\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSabina M, Alsamman MM. Pulse of Progress: A Systematic Review of Glucagon-Like Peptide-1 Receptor Agonists in Cardiovascular Health. Cardiol Res. 2024;15(1):1\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.14740/cr1600\u003c/span\u003e\u003cspan address=\"10.14740/cr1600\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaranowska-Bik A, Bik W. Insulin and brain aging. Prz Menopauzalny. 2017;16(2):44\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5114/pm.2017.68590\u003c/span\u003e\u003cspan address=\"10.5114/pm.2017.68590\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNinomiya T. Epidemiological Evidence of the Relationship Between Diabetes and Dementia. Adv Exp Med Biol. 2019;1128:13\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-981-13-3540-2_2\u003c/span\u003e\u003cspan address=\"10.1007/978-981-13-3540-2_2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchrijvers EM, Witteman JC, Sijbrands EJ, et al. Insulin metabolism and the risk of Alzheimer disease: the Rotterdam Study. Neurology. 2010;75(22):1982\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1212/WNL.0b013e3181ffe4f6\u003c/span\u003e\u003cspan address=\"10.1212/WNL.0b013e3181ffe4f6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHooshmand B, Rusanen M, Ngandu T, et al. Serum Insulin and Cognitive Performance in Older Adults: A Longitudinal Study. Am J Med. 2019;132(3):367\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.amjmed.2018.11.013\u003c/span\u003e\u003cspan address=\"10.1016/j.amjmed.2018.11.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEnnis GE, Koscik RL, Ma Y, Jonaitis EM, Van Hulle CA, Betthauser TJ, Randall AM, Chin N, Engelman CD, Anderson R, Suridjan I, Kollmorgen G, et al. Insulin resistance is related to cognitive decline but not change in CSF biomarkers of Alzheimer's disease in non-demented adults. Alzheimers Dement (Amst). 2021;13(1):e12220. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/dad2.12220\u003c/span\u003e\u003cspan address=\"10.1002/dad2.12220\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFessel J. Cure of Alzheimer's Dementia in Many Patients by Using Intranasal Insulin to Augment an Inadequate Counter-Reaction, Edaravone to Scavenge ROS, and 1 or 2 Other Drugs to Address Affected Brain Cells. J Clin Med. 2023;12(9):3151. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/jcm12093151\u003c/span\u003e\u003cspan address=\"10.3390/jcm12093151\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEuser SM, Sattar N, Witteman JC, et al. A prospective analysis of elevated fasting glucose levels and cognitive function in older people: results from PROSPER and the Rotterdam Study. Diabetes. 2010;59(7):1601\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/db09-0568\u003c/span\u003e\u003cspan address=\"10.2337/db09-0568\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCohen E, Dillin A. The insulin paradox: aging, proteotoxicity and neurodegeneration. Nat Rev Neurosci. 2008;9(10):759\u0026ndash;67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nrn2474\u003c/span\u003e\u003cspan address=\"10.1038/nrn2474\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKoshiyama H. Explanation of the insulin paradox from the evolutionary point of view. Jpn Clin Med. 2012;3:21\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4137/JCM.S10274\u003c/span\u003e\u003cspan address=\"10.4137/JCM.S10274\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZeng Y, Feng Q, Hesketh T, et al. Survival, disabilities in activities of daily living, and physical and cognitive functioning among the oldest-old in China: a cohort study. Lancet. 2017;389(10079):1619\u0026ndash;29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(17)30548-2\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(17)30548-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu J, Zhu X, Liu Y, et al. Association between triglyceride glucose index and suicide attempts in patients with first-episode drug-na\u0026iuml;ve major depressive disorder. Front Psychiatry. 2023;14:1231524. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpsyt.2023.1231524\u003c/span\u003e\u003cspan address=\"10.3389/fpsyt.2023.1231524\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu J, Jia F, Li C, et al. Association between body mass index and suicide attempts in Chinese patients of a hospital in Shanxi district with first-episode drug-na\u0026iuml;ve major depressive disorder. J Affect Disord. 2023;339:377\u0026ndash;83. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jad.2023.06.064\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2023.06.064\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee SH, Park SY, Choi CS. Insulin Resistance: From Mechanisms to Therapeutic Strategies. Diabetes Metab J. 2022;46(1):15\u0026ndash;37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4093/dmj.2021.0280\u003c/span\u003e\u003cspan address=\"10.4093/dmj.2021.0280\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang M, Tan Y, Shi Y, et al. Diabetes and Sarcopenic Obesity: Pathogenesis, Diagnosis, and Treatments. Front Endocrinol (Lausanne). 2020;11:568. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fendo.2020.00568\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2020.00568\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVersace S, Pellitteri G, Sperotto R, et al. A State-of-Art Review of the Vicious Circle of Sleep Disorders, Diabetes and Neurodegeneration Involving Metabolism and Microbiota Alterations. Int J Mol Sci. 2023;24(13):10615. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms241310615\u003c/span\u003e\u003cspan address=\"10.3390/ijms241310615\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMinh HV, Tien HA, Sinh CT, et al. Assessment of preferred methods to measure insulin resistance in Asian patients with hypertension. J Clin Hypertens (Greenwich). 2021;23(3):529\u0026ndash;37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jch.14155\u003c/span\u003e\u003cspan address=\"10.1111/jch.14155\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCaiati C, Stanca A, Lepera ME. Free Radicals and Obesity-Related Chronic Inflammation Contrasted by Antioxidants: A New Perspective in Coronary Artery Disease. Metabolites. 2023;13(6):712. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/metabo13060712\u003c/span\u003e\u003cspan address=\"10.3390/metabo13060712\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDowner B, Kumar A, Mehta H, et al. The Effect of Undiagnosed Diabetes on the Association Between Self-Reported Diabetes and Cognitive Impairment Among Older Mexican Adults. Am J Alzheimers Dis Other Demen. 2016;31(7):564\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/1533317516653824\u003c/span\u003e\u003cspan address=\"10.1177/1533317516653824\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang X, Chen Q, Liu Y, et al. Causal relationship between multiparameter brain MRI phenotypes and age: evidence from Mendelian randomization. Brain Commun. 2024;6(2):fcae077. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/braincomms/fcae077\u003c/span\u003e\u003cspan address=\"10.1093/braincomms/fcae077\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTaylor EN, Huang N, Wisco J, et al. The brains of aged mice are characterized by altered tissue diffusion properties and cerebral microbleeds. J Transl Med. 2020;18(1):277. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12967-020-02441-6\u003c/span\u003e\u003cspan address=\"10.1186/s12967-020-02441-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGul A, Sehar M, Jawad U. Relationship between Neuropsychological Functioning, Behavioral Inhibition, and Glycemic Control in Type 2 Diabetes: Findings from Task-switching Study. Chin Med J (Engl). 2017;130(4):487\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4103/0366-6999.199842\u003c/span\u003e\u003cspan address=\"10.4103/0366-6999.199842\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmith PP, Kuchel GA, Griffiths D. Functional Brain Imaging and the Neural Basis for Voiding Dysfunction in Older Adults. Clin Geriatr Med. 2015;31(4):549\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cger.2015.06.010\u003c/span\u003e\u003cspan address=\"10.1016/j.cger.2015.06.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNordstr\u0026ouml;m CH, Forsse A, Jakobsen RP, et al. Bedside interpretation of cerebral energy metabolism utilizing microdialysis in neurosurgical and general intensive care. Front Neurol. 2022;13:968288. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fneur.2022.968288\u003c/span\u003e\u003cspan address=\"10.3389/fneur.2022.968288\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDimitriadis GD, Maratou E, Kountouri A, et al. Regulation of Postabsorptive and Postprandial Glucose Metabolism by Insulin-Dependent and Insulin-Independent Mechanisms: An Integrative Approach. Nutrients. 2021;13(1):159. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/nu13010159\u003c/span\u003e\u003cspan address=\"10.3390/nu13010159\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVinson JA, Chen X, Garver DD. Determination of Total Chlorogenic Acids in Commercial Green Coffee Extracts. J Med Food. 2019;22(3):314\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/jmf.2018.0039\u003c/span\u003e\u003cspan address=\"10.1089/jmf.2018.0039\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMohseni S. Hypoglycemic neuropathy. Acta Neuropathol. 2001;102(5):413\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s004010100459\u003c/span\u003e\u003cspan address=\"10.1007/s004010100459\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTakahashi N, Sakurai T, Davis KL, et al. Linking oligodendrocyte and myelin dysfunction to neurocircuitry abnormalities in schizophrenia. Prog Neurobiol. 2011;93(1):13\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.pneurobio.2010.09.004\u003c/span\u003e\u003cspan address=\"10.1016/j.pneurobio.2010.09.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"insulin resistance, retrospective cohort study, triglyceride glucose index, cognitive impairment, older","lastPublishedDoi":"10.21203/rs.3.rs-7178589/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7178589/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eThe relationship between insulin resistance (IR) and susceptibility to cognitive decline remains unclear, with conflicting research findings. This nationwide retrospective analysis sought to examine the relationship between cognitive impairment and the triglyceride-glucose (TyG) index, a surrogate marker for IR, among older Chinese individuals without diabetes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eData analysis was conducted using information derived from the 2014\u0026ndash;2018 cohort of the Chinese Longitudinal Healthy Longevity Survey (CLHLS), comprising 988 non-diabetic adults whose mean age was 79.78 years (SD\u0026thinsp;=\u0026thinsp;9.21). The study population included participants, 572 were male (57.89%) and 416 were female (42.11%). The Mini-Mental State Examination (MMSE) was employed to evaluate cognitive function. Multivariate Cox proportional hazards models were utilized to assess the association between the TyG index and the likelihood of cognitive impairment. To investigate potential threshold effects, a two-piecewise Cox regression approach was implemented. Furthermore, the study incorporated interaction and stratified analyses, taking into account factors such as age, gender, marital status, exercise habits, smoking, and alcohol consumption.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAfter a 4-year follow-up period, 201 participants (20.3%) developed cognitive impairment, despite having normal cognition at baseline. Controlling for relevant variables, the multivariate Cox regression analysis did not reveal a statistically meaningful link between cognitive decline and the TyG score (HR\u0026thinsp;=\u0026thinsp;0.77, 95% CI: 0.56\u0026ndash;1.06, P\u0026thinsp;=\u0026thinsp;0.106). However, smoothing plots suggested that the relationship between cognitive impairment and the TyG index was non-linear, with a turning point identified at 7.57. Above this inflection point, a negative association was observed (HR\u0026thinsp;=\u0026thinsp;0.67, 95% CI: 0.50\u0026ndash;0.91, P\u0026thinsp;=\u0026thinsp;0.009), whereas no notable association was found below it (P\u0026thinsp;=\u0026thinsp;0.75).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eOur research uncovers a nuanced, non-linear association linking the TyG index to cognitive decline among elderly individuals without diabetes. These results offer valuable insights with implications for informing public health strategies and policy development.\u003c/p\u003e","manuscriptTitle":"TyG Index and Cognitive Decline in Non-diabetic Elderly: Evidence from CLHLS 2014-2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-22 12:08:17","doi":"10.21203/rs.3.rs-7178589/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8d74f6c0-0122-4cf0-8177-6bb2a5e775b0","owner":[],"postedDate":"August 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-01T08:58:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-22 12:08:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7178589","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7178589","identity":"rs-7178589","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}